Abstract
In the framework of spin glass models with symmetric interactions a local dynamical learning process is studied, by which the energy landscape is modified in such a way that even strongly correlated noisy patterns can be recognized. Additionally the basins of attraction of the patterns can be systematically enlarged. After completion of the learning process the system can recognize as many patterns as there are neurons (p = N), and for small systems even more (p > N). The dependence of the learning time R on the parameters of the system (e.g., the average correlation, the noise level, and the number p of patterns) is studied and it is found that R increases as px, with x ≈ 3.5, as long as p < N, whereas for p > N the increase is more drastic.